AI Managed Services That Keep Your Production AI Running

Most AI projects do not fail at launch. They fail in the months that follow, when models drift, cloud costs climb and nobody owns the system day to day. KriraAI's AI managed services give you a dedicated team that runs, monitors and improves your production AI under a clear SLA, so every model keeps delivering the outcome it was built for.

What Are AI Managed Services?

AI managed services are an outsourced operating model in which a specialist provider takes ongoing responsibility for your AI systems after they go live. The provider monitors model performance, retrains and updates models, manages infrastructure and spend, and enforces governance and security policies. Your organization keeps full ownership of its models, data and intellectual property while the provider handles daily operations.

Managed AI services should not be confused with traditional managed IT, which keeps networks, servers and devices running. They are also different from AIOps, which applies AI to automate IT operations. We work in the opposite direction: we operate the AI itself, from classical machine learning models to large language model applications and autonomous agents.

Why Enterprises Need Managed AI Solutions After Deployment

Deploying a model is the beginning of its lifecycle, not the end. Live data shifts, user behaviour changes and the cost of serving predictions at scale is rarely what the pilot suggested. Without a structured operating layer, enterprises usually run into four recurring problems.

Model drift

accuracy quietly declines as data patterns change, and no alert fires until a business metric drops.

Talent gaps

MLOps and LLMOps engineers are scarce, expensive to hire and hard to retain.

Rising costs

unoptimized inference, idle GPUs and growing token usage inflate cloud bills month after month.

Compliance risk

regulators and internal policies demand audit trails, explainability and access controls that most teams lack the bandwidth to maintain.

Our AI Managed Services

Our managed AI solutions cover the full operating lifecycle of production AI. You can engage us for a single capability or hand over end to end ownership of your AI estate. Every engagement includes defined SLAs, named engineers and monthly performance reporting.

MLOps Services and MLOps as a Service

We build and run the pipelines that move models from experimentation to production reliably. This includes automated training and retraining workflows, versioned model registries, CI/CD for models and reproducible environments. Teams that already have models in production can adopt our MLOps as a service model and plug existing assets into a managed pipeline without rebuilding them.

LLMOps Services for Generative AI

Generative AI introduces operational challenges that classical MLOps was never designed for. Our LLMOps services cover prompt and version management, retrieval pipeline tuning, output quality evaluation, guardrails and token cost control across providers. As part of our generative AI managed services, we also handle model upgrades so your applications benefit from newer models without breaking in production.

AI Model Monitoring and Performance Management

Our AI model monitoring tracks accuracy, data drift, concept drift, latency and fairness metrics around the clock. Alerts are tied to business thresholds, so issues are flagged before they affect customers or revenue. When performance degrades, our engineers diagnose the root cause and retrain, roll back or recalibrate the model within an agreed response time.

AI Agent Management and Optimization

Autonomous agents need closer supervision than static models because they take actions, call tools and chain decisions together. Our AI agent management covers task success tracking, tool call auditing, failure recovery and workflow optimization. We continuously refine agent instructions and integrations so agents become more reliable and cost efficient over time.

AI Support and Maintenance Services

Our AI support and maintenance services keep the surrounding system healthy, not just the model. We manage dependency updates, API changes, infrastructure scaling, security patches and integration fixes across your stack. Support runs on tiered SLAs, with 24x7 coverage available for business critical workloads.

AI Governance and Compliance Services

Our AI governance services give leadership and auditors confidence in every model you run. We implement model documentation, audit trails, access controls, bias checks and explainability reporting aligned with frameworks such as the EU AI Act, ISO/IEC 42001 and India's DPDP Act. Governance is built into daily operations rather than treated as a one time exercise.

How Our Managed AI Engagement

Works

Every managed AI engagement follows a structured four step model. It gives you full visibility from the first assessment through to steady state operations. Timelines depend on the number and complexity of models in scope.

Assess

We audit your models, data pipelines, infrastructure and governance posture, then share a gap report and operating plan.

Onboard

We set up monitoring dashboards, alerting, runbooks and access controls, and document every system in scope.

Operate

Our engineers run your AI systems day to day under agreed SLAs, with clear escalation paths.

Optimize

Monthly reviews identify accuracy, cost and reliability improvements, which we deliver on a planned roadmap.

AI Managed Services vs In-House Team vs AI Consulting

Factor

AI Managed Services

In-House Team

AI Consulting

Upfront cost

Low, predictable monthly fee

High hiring and tooling cost

Project based fees

Time to value

Fast, uses proven playbooks

Slow, depends on hiring

Fast for strategy only

Expertise depth

Full MLOps, LLMOps and governance team

Limited to the people you hire

Strong advice, limited execution

Ongoing accountability

Backed by SLAs

Internal only

Ends with the project

Scalability

Scales with your AI estate

Requires new hires

Not built for ongoing operations

An in-house team makes sense when AI is your core product and you can hire and retain specialists. Consulting works well for strategy and roadmaps but usually stops before day to day operations. For most enterprises running several production models, an AI managed service provider offers the best balance of expertise, cost and accountability.

Why Choose KriraAI as Your AI Managed Service Provider

KriraAI builds and deploys production grade AI systems for enterprise clients across 22+ industries, so we operate AI with the perspective of the people who build it. That experience shapes how we monitor, troubleshoot and improve every system we manage. As an AI managed services company, we measure success by business outcomes rather than ticket counts.

Build to Run Expertise

Our engineers have designed the same kinds of systems they now operate.

AI Managed Services for India and Global Enterprises

KriraAI is headquartered in Surat, Gujarat, and supports enterprises across India and international markets. Our delivery model provides overlapping coverage for clients in North America, Europe, the Middle East and Asia Pacific. For organizations looking for AI managed services in India, we also support local data residency and compliance with Indian data protection rules.

Ready to Hand Off Your AI Operations?

Your AI should get better after launch, not quietly decline. Book a free AI ops assessment and our team will review your models, pipelines and costs, then share a clear operating plan. There is no obligation, and you will leave with practical recommendations either way.

Frequently Asked Questions About AI Managed

Services

AI managed services typically include model monitoring, retraining, MLOps pipeline management, infrastructure and cost management, security, governance and ongoing support. At KriraAI, scope is defined per engagement and can cover a single model or your entire AI estate. Every plan includes SLAs, named engineers and monthly performance reports.

AIOps uses artificial intelligence to automate IT operations such as incident detection and log analysis. AI managed services work the other way around by operating and maintaining AI systems themselves. A company running production machine learning or generative AI applications needs AI managed services, not AIOps.

The cost of managed AI services depends on the number of models in scope, their complexity, required coverage hours and SLA response times. Most providers, including KriraAI, charge a predictable monthly fee instead of hourly billing. A free assessment is the fastest way to get an accurate quote for your environment.

Organizations that run AI in production but lack a dedicated MLOps or LLMOps team benefit most from an AI managed service provider. This includes enterprises scaling beyond a few pilot models and companies facing compliance requirements for AI. Teams that want their internal engineers focused on new products rather than maintenance are also strong candidates.

Yes, our MLOps as a service model is designed for teams that already have models in production. We connect existing models, data sources and infrastructure to a managed pipeline without forcing a rebuild. You gain automated retraining, versioning and monitoring within your current stack.

Yes, our generative AI managed services cover chatbots, copilots, retrieval augmented generation systems and other LLM applications. Our LLMOps services include prompt versioning, output quality evaluation, guardrails, provider switching and token cost control. We support both commercial model APIs and self hosted open source models.

Our AI model monitoring continuously compares live data and predictions against training baselines to detect data drift and concept drift. When metrics cross agreed thresholds, engineers investigate the cause and decide whether to retrain, recalibrate or roll back the model. Every intervention is documented for audit and reporting.

Our AI governance services embed documentation, audit trails, access controls, bias testing and explainability into daily operations. Controls are mapped to frameworks such as the EU AI Act, ISO/IEC 42001 and India's DPDP Act, depending on where you operate. This keeps your AI audit ready at all times rather than only during reviews.

Yes, you retain full ownership of all models, data, code and intellectual property throughout the engagement. KriraAI operates within your cloud accounts or agreed environments using role based access. If you later choose to bring operations in house, we provide complete documentation and a structured handover.

Yes, KriraAI provides AI managed services in India from its headquarters in Surat, Gujarat, and serves clients across the country. We also support global enterprises in North America, Europe, the Middle East and Asia Pacific. Indian clients can choose local data residency and compliance aligned with Indian data protection rules.